Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
arXiv preprint arXiv:2005.10881 , year=
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Presents a systematic framework for evaluating MIAs across the full ML pipeline with standardized threat models and complementary metrics for different cost scenarios.
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Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
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Presents a systematic framework for evaluating MIAs across the full ML pipeline with standardized threat models and complementary metrics for different cost scenarios.